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                        Introduction
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                            <b>1.</b>
                        
                        第一章：数据分析前奏
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                            <b>1.1.</b>
                        
                        第1节：什么是数据分析
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                            <b>1.2.</b>
                        
                        第2节：环境搭建
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                            <b>1.3.</b>
                        
                        第3节：jupyternotebook使用
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                            <b>1.4.</b>
                        
                        第4节：作业
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                            <b>2.</b>
                        
                        第二章：Numpy库
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                        第1节：numpy库介绍
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                            <b>2.2.</b>
                        
                        第2节：numpy数组基本
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                            <b>2.3.</b>
                        
                        第3节：numpy数组操作
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                            <b>2.4.</b>
                        
                        第4节：numpy索引和切片
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                            <b>2.5.</b>
                        
                        第5节：Numpy索引和切片作业
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                            <b>2.6.</b>
                        
                        第6节：numpy数组操作
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                            <b>2.7.</b>
                        
                        第7节：深拷贝和浅拷贝
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                            <b>2.8.</b>
                        
                        第8节：文件操作
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                            <b>2.9.</b>
                        
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                            <b>2.10.</b>
                        
                        第10节：NAN和INF值处理.md
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                            <b>2.11.</b>
                        
                        第11节：random模块.md
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                        第12节：axis理解.md
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                        第14节：numpy练习题.md
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                            <b>3.</b>
                        
                        第三章：Pandas库
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                        第1节：pandas介绍
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                            <b>3.2.</b>
                        
                        第2节：pandas索引操作
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                            <b>3.3.</b>
                        
                        第3节：pandas对齐运算
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                            <b>3.4.</b>
                        
                        第4节：pandas函数应用
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                            <b>3.5.</b>
                        
                        第5节：pandas层级索引
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                            <b>3.6.</b>
                        
                        第6节：pandas统计计算和描述
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                            <b>3.7.</b>
                        
                        第7节：文件操作
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                            <b>3.8.</b>
                        
                        第8节：数据清洗
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                            <b>3.9.</b>
                        
                        第9节：聚合和分组
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                            <b>3.10.</b>
                        
                        第10节：时间序列
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                            <b>4.</b>
                        
                        第四章：Matploblib库
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                            <b>4.1.</b>
                        
                        第1节：常用图
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                            <b>4.2.</b>
                        
                        第2节：基本使用
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                            <b>4.3.</b>
                        
                        第3节：条形图
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                            <b>4.4.</b>
                        
                        第4节：直方图
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                            <b>4.5.</b>
                        
                        第5节：散点图
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                            <b>4.6.</b>
                        
                        第6节：饼图
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                            <b>4.7.</b>
                        
                        第7节：箱线图
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                            <b>4.8.</b>
                        
                        第8节：雷达图
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                            <b>4.9.</b>
                        
                        第9节：matplotlib绘图分析
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                            <b>4.10.</b>
                        
                        第10节：多图布局
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                            <b>4.11.</b>
                        
                        第11节：matplotlib配置
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                            <b>4.12.</b>
                        
                        第12节：matplotlib作业
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                            <b>5.</b>
                        
                        第五章：Seaborn库
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                            <b>5.1.</b>
                        
                        第1节：关系绘图
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                            <b>5.2.</b>
                        
                        第2节：分类绘图
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                            <b>5.3.</b>
                        
                        第3节：分布绘图
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                            <b>5.4.</b>
                        
                        第4节：线性关系绘图
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                            <b>5.5.</b>
                        
                        第5节：FacetGrid结构图
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                            <b>5.6.</b>
                        
                        第6节：样式设置
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                            <b>5.7.</b>
                        
                        第7节：调色盘设置
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                            <b>5.8.</b>
                        
                        第8节：seaborn作业
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                            <b>6.</b>
                        
                        第六章：统计分析强化
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                            <b>6.1.</b>
                        
                        第1节：常用专业数学术语
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                            <b>6.2.</b>
                        
                        第2节：平均数
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                            <b>6.3.</b>
                        
                        第3节：方差和标准差
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                            <b>6.4.</b>
                        
                        第4节：正态分布
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                            <b>6.5.</b>
                        
                        第5节：对比分析
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                            <b>6.6.</b>
                        
                        第6节：分布分析
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                            <b>6.7.</b>
                        
                        第7节：交叉分析
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                            <b>6.8.</b>
                        
                        第8节：统计分析
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                            <b>6.9.</b>
                        
                        第9节：帕累托分析
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                            <b>6.10.</b>
                        
                        第10节：矩阵关联分析
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                            <b>6.11.</b>
                        
                        第11节：综合性分析
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                            <b>7.</b>
                        
                        第七章：数据分析实战
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                            <b>7.1.</b>
                        
                        第1节：App数据分析实战
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                            <b>7.2.</b>
                        
                        第2节：心脏病患者数据分析
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                            <b>7.3.</b>
                        
                        第3节：StackoverFlow数据分析
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                            <b>7.4.</b>
                        
                        第4节：二手房数据分析
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                            <b>7.5.</b>
                        
                        第5节：吃鸡数据分析
                    </a>
            
            
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                            <b>7.6.</b>
                        
                        第6节：黑色星期五数据分析
                    </a>
            
            
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            </ul>
            
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                            <b>8.</b>
                        
                        第八章：补充
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                            <b>8.1.</b>
                        
                        第1节：用Excel做数据分析
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                            <b>8.2.</b>
                        
                        第2节：echarts和pyecharts库
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                            <b>8.3.</b>
                        
                        第3节：bokeh库
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                        <h1 id="&#x6563;&#x70B9;&#x56FE;">&#x6563;&#x70B9;&#x56FE;</h1>
<p>&#x6563;&#x70B9;&#x56FE;&#x4E5F;&#x53EB; X-Y &#x56FE;&#xFF0C;&#x5B83;&#x5C06;&#x6240;&#x6709;&#x7684;&#x6570;&#x636E;&#x4EE5;&#x70B9;&#x7684;&#x5F62;&#x5F0F;&#x5C55;&#x73B0;&#x5728;&#x76F4;&#x89D2;&#x5750;&#x6807;&#x7CFB;&#x4E0A;&#xFF0C;&#x4EE5;&#x663E;&#x793A;&#x53D8;&#x91CF;&#x4E4B;&#x95F4;&#x7684;&#x76F8;&#x4E92;&#x5F71;&#x54CD;&#x7A0B;&#x5EA6;&#xFF0C;&#x70B9;&#x7684;&#x4F4D;&#x7F6E;&#x7531;&#x53D8;&#x91CF;&#x7684;&#x6570;&#x503C;&#x51B3;&#x5B9A;&#x3002;</p>
<p>&#x901A;&#x8FC7;&#x89C2;&#x5BDF;&#x6563;&#x70B9;&#x56FE;&#x4E0A;&#x6570;&#x636E;&#x70B9;&#x7684;&#x5206;&#x5E03;&#x60C5;&#x51B5;&#xFF0C;&#x6211;&#x4EEC;&#x53EF;&#x4EE5;&#x63A8;&#x65AD;&#x51FA;&#x53D8;&#x91CF;&#x95F4;&#x7684;&#x76F8;&#x5173;&#x6027;&#x3002;&#x5982;&#x679C;&#x53D8;&#x91CF;&#x4E4B;&#x95F4;&#x4E0D;&#x5B58;&#x5728;&#x76F8;&#x4E92;&#x5173;&#x7CFB;&#xFF0C;&#x90A3;&#x4E48;&#x5728;&#x6563;&#x70B9;&#x56FE;&#x4E0A;&#x5C31;&#x4F1A;&#x8868;&#x73B0;&#x4E3A;&#x968F;&#x673A;&#x5206;&#x5E03;&#x7684;&#x79BB;&#x6563;&#x7684;&#x70B9;&#xFF0C;&#x5982;&#x679C;&#x5B58;&#x5728;&#x67D0;&#x79CD;&#x76F8;&#x5173;&#x6027;&#xFF0C;&#x90A3;&#x4E48;&#x5927;&#x90E8;&#x5206;&#x7684;&#x6570;&#x636E;&#x70B9;&#x5C31;&#x4F1A;&#x76F8;&#x5BF9;&#x5BC6;&#x96C6;&#x5E76;&#x4EE5;&#x67D0;&#x79CD;&#x8D8B;&#x52BF;&#x5448;&#x73B0;&#x3002;&#x6570;&#x636E;&#x7684;&#x76F8;&#x5173;&#x5173;&#x7CFB;&#x4E3B;&#x8981;&#x5206;&#x4E3A;&#xFF1A;&#x6B63;&#x76F8;&#x5173;&#xFF08;&#x4E24;&#x4E2A;&#x53D8;&#x91CF;&#x503C;&#x540C;&#x65F6;&#x589E;&#x957F;&#xFF09;&#x3001;&#x8D1F;&#x76F8;&#x5173;&#xFF08;&#x4E00;&#x4E2A;&#x53D8;&#x91CF;&#x503C;&#x589E;&#x52A0;&#x53E6;&#x4E00;&#x4E2A;&#x53D8;&#x91CF;&#x503C;&#x4E0B;&#x964D;&#xFF09;&#x3001;&#x4E0D;&#x76F8;&#x5173;&#x3001;&#x7EBF;&#x6027;&#x76F8;&#x5173;&#x3001;&#x6307;&#x6570;&#x76F8;&#x5173;&#x7B49;&#xFF0C;&#x8868;&#x73B0;&#x5728;&#x6563;&#x70B9;&#x56FE;&#x4E0A;&#x7684;&#x5927;&#x81F4;&#x5206;&#x5E03;&#x5982;&#x4E0B;&#x56FE;&#x6240;&#x793A;&#x3002;&#x90A3;&#x4E9B;&#x79BB;&#x70B9;&#x96C6;&#x7FA4;&#x8F83;&#x8FDC;&#x7684;&#x70B9;&#x6211;&#x4EEC;&#x79F0;&#x4E3A;&#x79BB;&#x7FA4;&#x70B9;&#x6216;&#x8005;&#x5F02;&#x5E38;&#x70B9;&#x3002;<br><img src="../assets/chapter04/&#x6563;&#x70B9;&#x56FE;&#x76F8;&#x5173;&#x6027;.png" alt=""></p>
<p>&#x793A;&#x4F8B;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter05/&#x6563;&#x70B9;&#x56FE;&#x793A;&#x4F8B;.jpg" alt=""></p>
<h2 id="&#x7ED8;&#x5236;&#x6563;&#x70B9;&#x56FE;&#xFF1A;">&#x7ED8;&#x5236;&#x6563;&#x70B9;&#x56FE;&#xFF1A;</h2>
<p>&#x6563;&#x70B9;&#x56FE;&#x7684;&#x7ED8;&#x5236;&#xFF0C;&#x4F7F;&#x7528;&#x7684;&#x662F;<code>plt.scatter</code>&#x65B9;&#x6CD5;&#xFF0C;&#x8FD9;&#x4E2A;&#x65B9;&#x6CD5;&#x6709;&#x4EE5;&#x4E0B;&#x53C2;&#x6570;&#xFF1A;</p>
<ol>
<li><code>x,y</code>&#xFF1A;&#x5206;&#x522B;&#x662F;x&#x8F74;&#x548C;y&#x8F74;&#x7684;&#x6570;&#x636E;&#x96C6;&#x3002;&#x4E24;&#x8005;&#x7684;&#x6570;&#x636E;&#x957F;&#x5EA6;&#x5FC5;&#x987B;&#x4E00;&#x81F4;&#x3002;</li>
<li><code>s</code>&#xFF1A;&#x70B9;&#x7684;&#x5C3A;&#x5BF8;&#x3002;&#x5982;&#x679C;&#x662F;&#x4E00;&#x4E2A;&#x5177;&#x4F53;&#x7684;&#x6570;&#x5B57;&#xFF0C;&#x90A3;&#x4E48;&#x6563;&#x70B9;&#x56FE;&#x7684;&#x6240;&#x6709;&#x70B9;&#x90FD;&#x662F;&#x4E00;&#x6837;&#x5927;&#x5C0F;&#xFF0C;&#x5982;&#x679C;&#x662F;&#x4E00;&#x4E2A;&#x5E8F;&#x5217;&#xFF0C;&#x90A3;&#x4E48;&#x8FD9;&#x4E2A;&#x5E8F;&#x5217;&#x7684;&#x957F;&#x5EA6;&#x5E94;&#x8BE5;&#x548C;x&#x8F74;&#x6570;&#x636E;&#x91CF;&#x4E00;&#x81F4;&#xFF0C;&#x5E8F;&#x5217;&#x4E2D;&#x7684;&#x6BCF;&#x4E2A;&#x5143;&#x7D20;&#x4EE3;&#x8868;&#x6BCF;&#x4E2A;&#x70B9;&#x7684;&#x5C3A;&#x5BF8;&#x3002;</li>
<li><code>c</code>&#xFF1A;&#x70B9;&#x7684;&#x989C;&#x8272;&#x3002;&#x53EF;&#x4EE5;&#x4E3A;&#x5177;&#x4F53;&#x7684;&#x989C;&#x8272;&#xFF0C;&#x4E5F;&#x53EF;&#x4EE5;&#x4E3A;&#x4E00;&#x4E2A;&#x5E8F;&#x5217;&#x6216;&#x8005;&#x662F;&#x4E00;&#x4E2A;<code>cmap</code>&#x5BF9;&#x8C61;&#x3002;</li>
<li><code>marker</code>&#xFF1A;&#x6807;&#x8BB0;&#x70B9;&#xFF0C;&#x9ED8;&#x8BA4;&#x662F;&#x5706;&#x70B9;&#xFF0C;&#x4E5F;&#x53EF;&#x4EE5;&#x6362;&#x6210;&#x5176;&#x4ED6;&#x7684;&#x3002;</li>
<li>&#x5176;&#x4ED6;&#x53C2;&#x6570;&#xFF1A;<code>https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html#matplotlib.pyplot.scatter</code>&#x3002;</li>
</ol>
<p>&#x6BD4;&#x5982;&#x6709;&#x4E00;&#x7EC4;&#x8FD0;&#x52A8;&#x5458;&#x8EAB;&#x9AD8;&#x548C;&#x4F53;&#x91CD;&#x4EE5;&#x53CA;&#x5E74;&#x9F84;&#x7684;&#x6570;&#x636E;&#xFF0C;&#x90A3;&#x4E48;&#x53EF;&#x4EE5;&#x901A;&#x8FC7;&#x4EE5;&#x4E0B;&#x4EE3;&#x7801;&#x6765;&#x7ED8;&#x5236;&#x6563;&#x70B9;&#x56FE;&#xFF1A;</p>
<pre><code class="lang-python">male_athletes = athletes[athletes[<span class="hljs-string">&apos;Sex&apos;</span>] == <span class="hljs-string">&apos;M&apos;</span>]
female_athletes = athletes[athletes[<span class="hljs-string">&apos;Sex&apos;</span>] == <span class="hljs-string">&apos;F&apos;</span>]
male_mean_height = male_athletes[<span class="hljs-string">&apos;Height&apos;</span>].mean()
female_mean_height = female_athletes[<span class="hljs-string">&apos;Height&apos;</span>].mean()
male_mean_weight = male_athletes[<span class="hljs-string">&apos;Weight&apos;</span>].mean()
female_mean_weight = female_athletes[<span class="hljs-string">&apos;Weight&apos;</span>].mean()

plt.figure(figsize=(<span class="hljs-number">10</span>,<span class="hljs-number">5</span>))
plt.scatter(male_athletes[<span class="hljs-string">&apos;Height&apos;</span>],male_athletes[<span class="hljs-string">&apos;Weight&apos;</span>],s=male_athletes[<span class="hljs-string">&apos;Age&apos;</span>],marker=<span class="hljs-string">&apos;^&apos;</span>,color=<span class="hljs-string">&apos;g&apos;</span>,label=<span class="hljs-string">&apos;&#x7537;&#x6027;&apos;</span>,alpha=<span class="hljs-number">0.5</span>)
plt.scatter(female_athletes[<span class="hljs-string">&apos;Height&apos;</span>],female_athletes[<span class="hljs-string">&apos;Weight&apos;</span>],color=<span class="hljs-string">&apos;r&apos;</span>,alpha=<span class="hljs-number">0.5</span>,s=female_athletes[<span class="hljs-string">&apos;Age&apos;</span>],label=<span class="hljs-string">&apos;&#x5973;&#x6027;&apos;</span>)
plt.axvline(male_mean_height,color=<span class="hljs-string">&quot;g&quot;</span>,linewidth=<span class="hljs-number">1</span>)
plt.axhline(male_mean_weight,color=<span class="hljs-string">&quot;g&quot;</span>,linewidth=<span class="hljs-number">1</span>)
plt.axvline(female_mean_height,color=<span class="hljs-string">&quot;r&quot;</span>,linewidth=<span class="hljs-number">1</span>)
plt.axhline(female_mean_weight,color=<span class="hljs-string">&quot;r&quot;</span>,linewidth=<span class="hljs-number">1</span>)
plt.xticks(np.arange(<span class="hljs-number">140</span>,<span class="hljs-number">220</span>,<span class="hljs-number">5</span>))
plt.yticks(np.arange(<span class="hljs-number">30</span>,<span class="hljs-number">150</span>,<span class="hljs-number">10</span>))
plt.legend(prop=font)
plt.xlabel(<span class="hljs-string">&quot;&#x8EAB;&#x9AD8;&#xFF08;cm&#xFF09;&quot;</span>,fontproperties=font)
plt.ylabel(<span class="hljs-string">&quot;&#x4F53;&#x91CD;&#xFF08;kg&#xFF09;&quot;</span>,fontproperties=font)
plt.title(<span class="hljs-string">&quot;&#x8FD0;&#x52A8;&#x5458;&#x8EAB;&#x9AD8;&#x548C;&#x4F53;&#x91CD;&#x6563;&#x70B9;&#x56FE;&quot;</span>,fontproperties=font)
plt.grid()
plt.show()
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x8FD0;&#x52A8;&#x5458;&#x6563;&#x70B9;&#x56FE;.png" alt=""></p>
<h2 id="&#x7ED8;&#x5236;&#x56DE;&#x5F52;&#x66F2;&#x7EBF;&#xFF1A;">&#x7ED8;&#x5236;&#x56DE;&#x5F52;&#x66F2;&#x7EBF;&#xFF1A;</h2>
<p>&#x6709;&#x4E00;&#x7EC4;&#x6570;&#x636E;&#x540E;&#xFF0C;&#x6211;&#x4EEC;&#x53EF;&#x4EE5;&#x5BF9;&#x8FD9;&#x7EC4;&#x6570;&#x636E;&#x8FDB;&#x884C;&#x56DE;&#x5F52;&#x5206;&#x6790;&#xFF0C;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x53EF;&#x4EE5;&#x5E2E;&#x52A9;&#x6211;&#x4EEC;&#x4E86;&#x89E3;&#x8FD9;&#x7EC4;&#x6570;&#x636E;&#x7684;&#x5927;&#x4F53;&#x8D70;&#x5411;&#x3002;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x6309;&#x7167;&#x6D89;&#x53CA;&#x7684;&#x53D8;&#x91CF;&#x7684;&#x591A;&#x5C11;&#xFF0C;&#x5206;&#x4E3A;&#x4E00;&#x5143;&#x56DE;&#x5F52;&#x548C;&#x591A;&#x5143;&#x56DE;&#x5F52;&#x5206;&#x6790;&#xFF1B;&#x6309;&#x7167;&#x81EA;&#x53D8;&#x91CF;&#x7684;&#x591A;&#x5C11;&#xFF0C;&#x53EF;&#x5206;&#x4E3A;&#x7B80;&#x5355;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x548C;&#x591A;&#x91CD;&#x56DE;&#x5F52;&#x5206;&#x6790;&#xFF1B;&#x6309;&#x7167;&#x81EA;&#x53D8;&#x91CF;&#x548C;&#x56E0;&#x53D8;&#x91CF;&#x4E4B;&#x95F4;&#x7684;&#x5173;&#x7CFB;&#x7C7B;&#x578B;&#xFF0C;&#x53EF;&#x5206;&#x4E3A;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x548C;&#x975E;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x3002;&#x5982;&#x679C;&#x5728;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x4E2D;&#xFF0C;&#x53EA;&#x5305;&#x62EC;&#x4E00;&#x4E2A;&#x81EA;&#x53D8;&#x91CF;&#x548C;&#x4E00;&#x4E2A;&#x56E0;&#x53D8;&#x91CF;&#xFF0C;&#x4E14;&#x4E8C;&#x8005;&#x7684;&#x5173;&#x7CFB;&#x53EF;&#x7528;&#x4E00;&#x6761;&#x76F4;&#x7EBF;&#x8FD1;&#x4F3C;&#x8868;&#x793A;&#xFF0C;&#x8FD9;&#x79CD;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x79F0;&#x4E3A;&#x4E00;&#x5143;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x3002;&#x5982;&#x679C;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x4E2D;&#x5305;&#x62EC;&#x4E24;&#x4E2A;&#x6216;&#x4E24;&#x4E2A;&#x4EE5;&#x4E0A;&#x7684;&#x81EA;&#x53D8;&#x91CF;&#xFF0C;&#x4E14;&#x81EA;&#x53D8;&#x91CF;&#x4E4B;&#x95F4;&#x5B58;&#x5728;&#x7EBF;&#x6027;&#x76F8;&#x5173;&#xFF0C;&#x5219;&#x79F0;&#x4E3A;&#x591A;&#x91CD;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x5206;&#x6790;&#x3002;</p>
<table>
<thead>
<tr>
<th>&#x81EA;&#x53D8;&#x91CF;&#x6570;&#x91CF;</th>
<th>&#x662F;&#x5426;&#x7EBF;&#x6027;</th>
<th>&#x56DE;&#x5F52;&#x7C7B;&#x578B;</th>
</tr>
</thead>
<tbody>
<tr>
<td>1&#x4E2A;</td>
<td>&#x662F;</td>
<td>&#x4E00;&#x5143;&#x7EBF;&#x6027;&#x56DE;&#x5F52;</td>
</tr>
<tr>
<td>&#x591A;&#x4E2A;</td>
<td>&#x662F;</td>
<td>&#x591A;&#x5143;&#x7EBF;&#x6027;&#x56DE;&#x5F52;</td>
</tr>
<tr>
<td>1&#x4E2A;</td>
<td>&#x5426;</td>
<td>&#x4E00;&#x5143;&#x975E;&#x7EBF;&#x6027;&#x56DE;&#x5F52;</td>
</tr>
<tr>
<td>&#x591A;&#x4E2A;</td>
<td>&#x5426;</td>
<td>&#x591A;&#x5143;&#x975E;&#x7EBF;&#x6027;&#x56DE;&#x5F52;</td>
</tr>
</tbody>
</table>
<p>&#x901A;&#x8FC7;&#x4EE5;&#x4E0A;&#x8FD0;&#x52A8;&#x5458;&#x6563;&#x70B9;&#x56FE;&#x7684;&#x5206;&#x6790;&#xFF0C;&#x6211;&#x4EEC;&#x603B;&#x4F53;&#x4E0A;&#x53EF;&#x4EE5;&#x770B;&#x51FA;&#x6765;&#x662F;&#x6EE1;&#x8DB3;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x7684;&#xFF0C;&#x56E0;&#x6B64;&#x53EF;&#x4EE5;&#x5728;&#x56FE;&#x4E0A;&#x7ED8;&#x5236;&#x4E00;&#x4E2A;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x7684;&#x7EBF;&#x6761;&#x3002;&#x60F3;&#x8981;&#x7ED8;&#x5236;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x7684;&#x7EBF;&#x6761;&#xFF0C;&#x9700;&#x8981;&#x5148;&#x6309;&#x7167;&#x4E4B;&#x524D;&#x7684;&#x6570;&#x636E;&#x8BA1;&#x7B97;&#x51FA;&#x7EBF;&#x6027;&#x65B9;&#x7A0B;&#xFF0C;&#x5047;&#x5982;<code>x</code>&#x662F;&#x81EA;&#x53D8;&#x91CF;&#xFF0C;<code>y</code>&#x662F;&#x56E0;&#x53D8;&#x91CF;&#xFF0C;&#x90A3;&#x4E48;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x7684;&#x65B9;&#x7A0B;&#x53EF;&#x4EE5;&#x7528;&#x4EE5;&#x4E0B;&#x51E0;&#x4E2A;&#x6765;&#x8868;&#x793A;&#xFF1A;</p>
<pre><code>y = &#x622A;&#x8DDD;+&#x659C;&#x7387;*x+&#x8BEF;&#x5DEE;
</code></pre><p>&#x53EA;&#x8981;&#x628A;&#x8FD9;&#x4E2A;&#x65B9;&#x7A0B;&#x8BA1;&#x7B97;&#x51FA;&#x6765;&#x4E86;&#xFF0C;&#x90A3;&#x4E48;&#x540E;&#x7EED;&#x6211;&#x4EEC;&#x5C31;&#x53EF;&#x4EE5;&#x6839;&#x636E;<code>x</code>&#x7684;&#x503C;&#xFF0C;&#x5927;&#x6982;&#x7684;&#x4F30;&#x8BA1;&#x51FA;<code>y</code>&#x7684;&#x53D6;&#x503C;&#x8303;&#x56F4;&#xFF0C;&#x4E5F;&#x5C31;&#x662F;&#x9884;&#x6D4B;&#x3002;&#x5982;&#x679C;&#x6211;&#x4EEC;&#x9488;&#x5BF9;&#x4EE5;&#x4E0A;&#x8FD0;&#x52A8;&#x5458;&#x7684;&#x8EAB;&#x9AD8;&#x548C;&#x4F53;&#x91CD;&#x7684;&#x5173;&#x7CFB;&#xFF0C;&#x53EA;&#x8981;&#x6709;&#x8EAB;&#x9AD8;&#xFF0C;&#x90A3;&#x4E48;&#x5C31;&#x53EF;&#x4EE5;&#x5927;&#x6982;&#x7684;&#x4F30;&#x8BA1;&#x51FA;&#x4F53;&#x91CD;&#x7684;&#x503C;&#x3002;&#x56DE;&#x5F52;&#x65B9;&#x7A0B;&#x7684;&#x7ED8;&#x5236;&#x6211;&#x4EEC;&#x9700;&#x8981;&#x501F;&#x52A9;<code>scikit-learn</code>&#x5E93;&#xFF0C;&#x8FD9;&#x4E2A;&#x5E93;&#x662F;&#x4E13;&#x95E8;&#x505A;&#x673A;&#x5668;&#x5B66;&#x4E60;&#x7528;&#x7684;&#xFF0C;&#x6211;&#x4EEC;&#x9700;&#x8981;&#x4F7F;&#x7528;&#x91CC;&#x9762;&#x7684;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x7C7B;<code>sklearn.liear_regression.LinearRegression</code>&#x3002;&#x793A;&#x4F8B;&#x4EE3;&#x7801;&#x5982;&#x4E0B;&#xFF1A;</p>
<pre><code class="lang-python"><span class="hljs-keyword">from</span> sklearn.linear_model <span class="hljs-keyword">import</span> LinearRegression
male_athletes = athletes[athletes[<span class="hljs-string">&apos;Sex&apos;</span>] == <span class="hljs-string">&apos;M&apos;</span>].dropna()
female_athletes = athletes[athletes[<span class="hljs-string">&apos;Sex&apos;</span>] == <span class="hljs-string">&apos;F&apos;</span>].dropna()
xtrain = male_athletes[<span class="hljs-string">&apos;Height&apos;</span>]
ytrain = male_athletes[<span class="hljs-string">&apos;Weight&apos;</span>]
<span class="hljs-comment"># &#x751F;&#x6210;&#x7EBF;&#x6027;&#x56DE;&#x5F52;&#x5BF9;&#x8C61;</span>
model = LinearRegression()
<span class="hljs-comment"># &#x5582;&#x8BAD;&#x7EC3;&#x6570;&#x636E;&#x8FDB;&#x53BB;&#xFF0C;&#x4F46;&#x662F;&#x9700;&#x8981;&#x628A;&#x56E0;&#x53D8;&#x91CF;&#x8F6C;&#x6362;&#x6210;1&#x5217;&#x591A;&#x884C;&#x7684;&#x6570;&#x636E;</span>
model.fit(xtrain[:,np.newaxis],ytrain)
<span class="hljs-comment"># &#x6253;&#x5370;&#x659C;&#x7387;</span>
print(model.coef_)
<span class="hljs-comment"># &#x6253;&#x5370;&#x622A;&#x8DDD;</span>
print(model.intercept_)
line_xticks = xtrain
<span class="hljs-comment"># &#x6839;&#x636E;&#x56DE;&#x5F52;&#x65B9;&#x7A0B;&#x8BA1;&#x7B97;&#x51FA;&#x7684;y&#x8F74;&#x5750;&#x6807;</span>
line_yticks = model.predict(xtrain[:,np.newaxis])
</code></pre>
<p>&#x6548;&#x679C;&#x56FE;&#x5982;&#x4E0B;&#xFF1A;<br><img src="../assets/chapter04/&#x6709;&#x56DE;&#x5F52;&#x7EBF;&#x7684;&#x8FD0;&#x52A8;&#x5458;&#x6563;&#x70B9;&#x56FE;.png" alt=""></p>

                    
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